/*
 * Copyright 2018 Analytics Zoo Authors.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.intel.analytics.zoo.pipeline.api.keras.objectives

import com.intel.analytics.bigdl.nn.MarginCriterion
import com.intel.analytics.bigdl.nn.abstractnn.AbstractCriterion
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.tensor.Tensor

import scala.reflect.ClassTag

/**
 * Creates a criterion that optimizes a  two-class classification
 * squared hinge loss (margin-based loss) between input x (a Tensor of dimension 1) and output y.
 *
 * @param margin if unspecified, is by default 1.
 * @param sizeAverage Boolean. Whether losses are averaged over observations for each
  *                   mini-batch. Default is true. If false, the losses are instead
  *                   summed for each mini-batch.
 */
class SquaredHinge[@specialized(Float, Double) T: ClassTag]
  (val margin: Double = 1.0, val sizeAverage: Boolean = true)
   (implicit ev: TensorNumeric[T]) extends TensorLossFunction[T] {

 override val loss: AbstractCriterion[Tensor[T], Tensor[T], T] =
   MarginCriterion(margin, sizeAverage, true)

}

object SquaredHinge {
  def apply[@specialized(Float, Double) T: ClassTag](
      margin: Double = 1.0,
      sizeAverage: Boolean = true)(implicit ev: TensorNumeric[T]) : SquaredHinge[T] = {
    new SquaredHinge[T](margin, sizeAverage)
  }
}
